Forecasting

ABSTRACT

A method for forecasting a performance characteristic of a game title is provided and includes selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user, generating base sales data for the game title responsive to initial sales data and generating forecast data for the game title responsive to the base sales data and the base game-play data.

RELATED APPLICATIONS

This application relates to U.S. Provisional Patent Application Ser. No. 60/923,264 (Atty. Docket No. IGA-0001-P), filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No. 60/923,344 (Atty. Docket No. IGA-0002-P), filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No. 60/923,345 (Atty. Docket No. IGA-0003-P), filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No. 60/923,346 (Atty. Docket No. IGA-0004-P), filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No. 60/923,351 (Atty. Docket No. IGA-0005-P), filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No. 60/923,352 (Atty. Docket No. IGA-0006-P), filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No. 60/923,353 (Atty. Docket No. IGA-0007-P), filed Apr. 12, 2007, all of which are incorporated by reference herein in their entireties.

FIELD OF THE INVENTION

This disclosure relates generally to in-game advertising and more particularly to a method for estimating desired parameters relevant to in-game advertising.

BACKGROUND OF THE INVENTION

As the placement of realistic advertisements in video games becomes more popular and acceptable in the gaming community, more and more video games are beginning to utilize video game advertisements as a viable source of revenue. Currently, most video games that employ realistic advertisements typically utilize a static advertising technique that involves placing each advertisement in one site throughout game play. As such, the location of the advertisement cannot change or move and other advertisements cannot take its place. Thus, although there may be multiple advertisements in one game, each advertisement can only occupy a single location throughout the entire game. This is undesirable because it lacks the ability to maximize the effect of the advertisement on the gamer.

One way to increase the effectiveness of the advertisement on the gamer is to utilize real-time dynamic advertising techniques which allow for the targeting of advertisements to specific gamers or groups of gamers. These dynamic advertising techniques allow multiple advertisements from different advertisers to be rotated through the same site during game play. Moreover, these dynamic advertising techniques allow for different content types, such as Billboard, Logo, Video, Audio and Beacons, to be used to display advertisements to the gamer. Each of these content types is capable of receiving and displaying multiple advertisements throughout the game for display to the gamer. For example, a racing game may have a billboard display advertising one product as the racing car goes around the curve and passes the billboard. However, subsequent times the race car goes around the curve and passes the billboard, entirely different advertisements may be displayed. Thus, dynamic advertising not only enhances the reality of the game's content, it maximizes the revenue generating capability of the software product by generating multiple revenue streams, as opposed to one revenue stream generated using static advertising techniques.

Unfortunately however, some problems currently exist with current approaches to in-game advertising. For example, because it is very difficult to determine the number of available users (i.e. gamers) and/or impressions that a game title is capable of delivering, it is very difficult to efficiently and effectively target advertisements to a specific audience.

SUMMARY OF THE INVENTION

A method for forecasting a performance characteristic of a game title is provided and includes selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user, generating base sales data for the game title responsive to initial sales data and generating forecast data for the game title responsive to the base sales data and the base game-play data.

A system for implementing a method for forecasting the performance of a game title is provided, where the includes a means for selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user, a means for generating base sales data for the game title responsive to initial sales data and a means for generating forecast data for the game title responsive to the base sales data and the base game-play data.

A computer readable storage medium having computer executable instructions for implementing a method for forecasting the performance of a game title is provided, where the method includes selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user, generating base sales data for the game title responsive to initial sales data and generating forecast data for the game title responsive to the base sales data and the base game-play data.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the present invention will be more fully understood from the following detailed description of illustrative embodiments, taken in conjunction with the accompanying figures in which like elements are numbered alike:

FIG. 1 is a high level schematic block diagram illustrating one embodiment of a gaming system, in accordance with the present invention.

FIG. 2 is a lower level schematic block diagram illustrating the integration server of the embodiment of the gaming system of FIG. 1, in accordance with the present invention.

FIG. 3 is a high level block diagram illustrating one embodiment of a method for forecasting a performance characteristic of a game title, in accordance with the present invention.

FIG. 4 is a lower level block diagram illustrating the method of FIG. 3, in accordance with the present invention.

FIG. 5 is a graph illustrating one embodiment of a “game-play curve,” in accordance with the present invention;

FIG. 6 is a graph illustrating another embodiment of a “game-play curve,” in accordance with the present invention;

FIG. 7 is a graph illustrating still another embodiment of a “game-play curve,” in accordance with the present invention;

FIG. 8 is a graph illustrating one embodiment of a “sales forecast curve,” in accordance with the present invention;

FIG. 9 is a graph illustrating one embodiment of a “seasonal sales modifier curve,” in accordance with the present invention;

FIG. 10 is a graph illustrating one embodiment of a “day of week modifier curve,” in accordance with the present invention;

FIG. 11 is a graph illustrating one embodiment of a “hours played/day curve,” in accordance with the present invention;

FIG. 12 is a graph illustrating one embodiment of “monthly sales information,” in accordance with the present invention;

FIG. 13 is a graph illustrating one embodiment of a “seasonal sales modifier,” in accordance with the present invention;

FIG. 14 is a graph illustrating one embodiment of a “sale forecast,” in accordance with the present invention;

FIG. 15 is a graph illustrating one embodiment of a “final sales curve,” in accordance with the present invention;

FIG. 16 is a graph illustrating one embodiment of a “life-cycle curve,” in accordance with the present invention;

FIG. 17 is a graph illustrating one embodiment of a “life-cycle curve,” with iterations, in accordance with the present invention

FIG. 18 is a graph illustrating one embodiment of a “base life-cycle curve,” in accordance with the present invention;

FIG. 19 is a graph illustrating one embodiment of a “day-of-week modifier curve,” in accordance with the present invention;

FIG. 20 is a graph illustrating one embodiment of a “life-cycle curve,” in accordance with the present invention; and

FIG. 21 is a graph illustrating one embodiment of an “impression forecast curve,” in accordance with the present invention.

DETAILED DESCRIPTION

The present invention allows for the accurate determination, or forecasting, of an audience for a game title over a given time period, such as for example the game title “GameX” for the month of April of a specific year. Thus, forecasting provides an accurate estimate as to the number of available users and impressions that a specific game title is capable of delivering. It should be appreciated that as referred to herein, advertising content means any type of advertising content, including but not limited to 3-Dimensional and/or holographic content.

In accordance with the present invention, one way forecasting may be accomplished is via algorithms that determine with a great deal of precision the audience for a title over a desired time period. This information may then be used, along with weighting information, to increase the effectiveness of an advertising campaign by delivering a requested number of impressions to a specifically targeted audience. It should be noted that the impression numbers are typically dependent upon the users making the forecasting accuracies only one component to success in in-game advertising. Accordingly, a sophisticated analytic engine is provided herein that is capable of accurately predicting out months (and/or years) at a time how many users and impressions a title is capable of delivering. This analytic engine helps advertisers plan an advertising campaign with an accurate degree of certainty, wherein the analytic engine may include a data warehouse that uses an analytical approach, such as an OLAP, to provide data, such as a comprehensive matrix of data, for a specific purpose, such as reporting and/or forecasting purposes.

The algorithm(s) used in forecasting may take into account seasonal game-play variations (i.e. variations in game-play activity due to seasons (summer, winter, fall, spring) and holidays (people tend to play more during holidays)) as well as day-of-week variations (day-of-week variations tend to be similar, but people tend to play more on weekends), using actual and/or predicted performance data as a feedback loop to modify forecasts on an ongoing dynamic basis, using actual and/or predicted data from similar games to generate forecast data and/or using pre-sales and/or post-sales data to determine the expected number of users that may be playing the game. One embodiment of the feedback loop works as follows: generate a forecast of the number of users, get actual user numbers and plug both of these values into the algorithm (and/or equation) to help generate more accurate data. This more accurate data can then be used to forecast similar games of similar genres, say for example two types of first-person shooter games.

It should be appreciated that the present invention is capable of determining inventory availability more precisely than current methods of random guessing. Additionally, the invention can also take into account marketing budgets, “buzz,” title genre, setting and/or game-play attributes, and/or uses one of a set of base game-play curves that describes the game-play pattern of an average user of that type of game. This base game-play curve can then be extrapolated to generate a life-cycle curve for the game, where the life-cycle curve is a larger curve created by combining sales forecasts (that are themselves determined by above factors). This life-cycle curve indicates the number of users expected to be playing a game for any date desired, such as a date within the game's life-time. An impression curve can also be generated based on testing of the game title. This impression curve is used to determine the number of impressions delivered within a typical session of game play. The impression curve can be combined with the life-cycle curve to provide the expected impressions for any date or time frame desired, such as that within the game's life-time. As actual game-play data is generated and/or recorded, this information can be fed back into the system (via a feedback loop) and used to adjust the forecast and/or delivery of content. It should be appreciated that theoretically there is a point where the forecast data and actual data converge to approximately (or exactly) the same values. This information may then be used to plan effective advertising campaigns.

In accordance with the present invention, although the concepts as discussed herein are discussed with regards to a gaming environment as follows, any type of gaming environment or configuration may be used. Referring to FIG. 1, one embodiment of a gaming system 10 for implementing the method of the invention showing the connectivity between the elements is shown and includes a user gaming device 20 having gaming software 30 and application software (SDK) 40, a gaming server 50 (optional) and an integration server 60 which includes advertiser information 70. In accordance with the present invention, a gaming server is optional and the game may be wholly or partially implemented via one or more computer(s) and/or gaming device(s) as desired. During gameplay, the gaming software 30 communicates with the gaming server 50 (optional) to facilitate the gameplay and the SDK 40 communicates with the integration server 60 to facilitate the integration of advertising content. Referring to FIG. 2, a lower level block diagram illustrating the elements of the integration server 60. As shown, the interaction within the integration server 60 is illustrated by a first set of arrows 75 which represents the flow of impressions through the integration server 60, a second set of arrows 80 which represents the flow of advertising content through the integration server 60 and a third set of arrows 85 which represents the flow of control messages (i.e. figuring out a user location, start session message, etc.) through the integration server 60.

In accordance with the present invention, one embodiment of a method 300 for forecasting the number of available users and/or impressions that a specific game title is capable of delivering is discussed hereinafter with regards to the performance of a specific game title in relation to the number of available users and impressions over the lifetime of the game (i.e. the title's forecast) and is illustrated as shown in FIG. 3 and FIG. 4. The method 300 includes selecting a base “game-play curve” for the game title from a set of pre-generated “game-play curves,” (see FIG. 5, FIG. 6, and FIG. 7 for examples) as shown in operational block 302, where the “game-play curve” is representative of how many hours a single average user would most likely play the game per day from the time the game was purchased/received until the end of the life of the game (i.e. no longer played).

The pre-generated “game-play curves” may be provided by the game publisher or generated based on test data, historical data, estimated data and/or predicted data as desired, such as for example a game title, genre and/or age group. The “game-play curve” may be selected from the set of pre-generated “game-play curves” based on one or more desired parameters, such as common characteristics between a specific pre-generated “game-play curve” and the game title being forecasted. For example, the pre-generated “game-play curves” may include a curve that is representative of an action game genre which involves a fantasy science fiction theme and that is targeted to the 15-18 year old age group. If the game title being forecasted is for an action game genre that is targeted to the 15-18 year old age group, then the aforementioned game-play curve may be selected. Additionally, if the pre-generated “game-play curves” also include a curve that is representative of an action game genre which involves a non-fantasy science fiction theme and that is targeted to the 15-18 year old age group, this game-play curve may be selected.

It is also contemplated that selected curves in the pre-generated set of “game-play curves” may be combined and/or used together to generate forecast data. Accordingly, the selected “game-play curve” may be selected based on various attributes of the title, including (but not limited to): Genre (i.e. Action, Driving, Shooter, Role-Playing), Distribution Type (i.e. Retail, Budget, Demo), Game-play (i.e. Single Player, Multiplayer), and Setting (i.e. Fantasy, Historic, Sci-Fi). This is possible because each attribute (or combination thereof) typically lends itself to different playing habits, which may ultimately be used to determine how many hours and at what frequency the game is played.

A base “sales curve” is also created, as shown in operational block 304, wherein the base “sales curve” is representative of how many units are expected and/or estimated to be sold during the life-time of the game and may be broken down into specific time periods, such as individual days. This helps to determine how many units of the game title are available and is usually directly related to the number of users available (typically a one-to-one relationship although the ratio may be different), as well as how long the game will be available. Creation of the base “sales curve” may be accomplished by taking sales forecast data (which may be furnished by the game publisher or obtained via other methods) (see FIG. 8 for example), actual sales data from previous versions of the game (if the game is a sequel), and/or actual sales data from similar games (i.e. games that may have the same or similar attributes used in determination of the “game-play curve”). Actual sales information may be generated by the user, provided by some entity that tracks such sales and/or provided by the publishers themselves. All or some of this data may be combined to form the “base sales curve,” where a weighted average may or may not be used.

The “base sales curve” may be modified to produce a “final sales curve,” as shown in operational block 306, wherein the modifier may be based on any number of desired characteristics, such as factors which affect the number of units “sold” over a period of time. For example, since the release date of the game is typically known in advance, the “sales curve” can be fixed to a specific period of time where Day 0 of the curve indicates the release date of the game. Accordingly, one modification to the “sales curve” may include adjustments to account for seasonal sales trends. One way this may be accomplished is by using a “seasonal sales modifier curve” (see FIG. 9 for example), where the “seasonal sales modifier curve” is a set of data (fixed or variable) that indicates representative for each day of the year (e.g. 1 through 365) of whether the game sales will be higher or lower than average, with a value of 1.0 typically indicating average. This modifier curve may be shifted (and repeated) so that the days of the year match those of the “fixed sales curve” and the values of the two curves can then be combined, for example multiplied together. Furthermore, it is contemplated that other modifier curves may be used to adjust the “base sales curve” in the same or similar manner, including but not limited to: a “marketing modifier curve” that indicates how the amount spent and methods of marketing the game title will affect the sales of the game over time, a “buzz modifier curve” that indicates how media attention to the game title will affect the sales of the game over time, a “pricing modifier curve” that indicates how adjustments in the pricing of the game title will affect the sales over time and a “piracy modifier curve” that indicates how rates of software piracy will typically affect the number of units of the game being played over time.

At this point, the “final sales curve” and the selected “base game-play curve” are combined to create a “final life-cycle curve,” as shown in operational block 308, which provides an indication of how many hours per day the game is expected to be played throughout its lifetime. The “final life-cycle curve” can be generated by iterating over a specific time period in the “final sales curve,” for example each day, taking the number of units expected to be “sold” on that day and multiplying the selected “game-play curve” by that number of units and plotting the results of the “final life-cycle curve” starting at the day of iteration. Next, the “forecast data” is generated, as shown in operational block 310, and may be accomplished by multiplying one or more additional modifiers in the same way that the “seasonal sales modifier curve” is used to modify the “base sales curve.” These modifiers however relate the number of hours played to time (instead of units “sold” to time), and may be generated and formatted similarly to the sales modifier curves. One such modifier may be the “day of week modifier curve” (see FIG. 10 for example) which indicates how the day of week on which the game is played will affect the number of hours that a user will play the game. It is contemplated that at this stage if no modifiers are desired, then the “final life-cycle curve” can be interpreted as the forecast data. The resultant forecast data is indicative of the number of available users that the game title will most likely generate.

It should be appreciated that before a game title is released, it may undergo a testing period that determines how many impressions per hour on average the title will be expected to generate. This may be accomplished in any number of ways, such as by playing the game as a normal user would and counting the number of impressions generated during each hour period, or through some other acceptable method. The “final life-cycle curve” is then multiplied by the impressions per hour value, to produce the forecast data for the number of available impressions that the game title will typically generate on a daily basis. Once the game title is released, the actual performance of the game title may not match that of the forecast values, so the forecast for future days may be modified to take this discrepancy into account. Actual performance data may include data sent to the system by the title and may include counts of impressions and/or users and may be stored in a database that allows easy access and/or search capabilities. This performance data can then be combined with the original forecast data for available users and impressions, by using a weighted average of the forecast and/or actual data, where the weight of the forecast data may initially be much higher than that of the actual data, but will typically decrease over time as the weight of the actual data increases over time as more actual data is accrued (i.e. the longer the game title is out, the more actual data is obtained). This process of adjusting the forecast can be repeated continuously (or in a predetermined fashion) as data is received for the game title. Typically, the forecast data and actual data will eventually converge within a small margin of error, such that the forecast data may be very close to what the game title will actually deliver.

In accordance with the invention, the method 300 for forecasting is illustrated with regards to the following example which assumes that the game title for which the forecast is being generated is directed to a single-player, shooter game set in the present day that will be distributed through retail channels starting at a predetermined time. Based on these attributes, a particular “game-play curve” is chosen as described hereinbefore. Typically performed by an analyst familiar with game-play styles, this selection may be based on only one characteristic of the game title. For this example, a curve as shown in FIG. 11 is chosen. This curve may be selected from a set of “standard” and/or pre-generated “game-play curves,” each of which provides for different types, genres and patterns of play. The curve that was selected is one in which game-play starts out at a constant level, and falls gradually over time. For this example, it is assumed that an average player will play the game for 33 days, with a maximum daily game-play of 5 hours and as such, the selected curve has been scaled to conform to these values.

The next (or preceding or concurrent) step in forecasting data for this game title is to combine data collected from various sources to generate a predictive “base sales curve.” It is contemplated that if no combination of data is desired then the original data could be used for the “base sales curve” or that no data is available, then the “base sales curve” could be generated with data already obtained at this point. Referring to FIG. 12, monthly sales information used to generate the sales forecast (shown as a thicker line) is illustrated and may combine information on sales of a previous version of the game, sales of similar games and/or sales predictions provided/generated by the publisher or other entity. In this example, these three sets of data are combined using a weighted average to generate the sales forecast. The data has been weighted such that the sales forecast is approximately 50% of the sales of the previous game, 30% of the sales of similar games, and 20% of the publisher sales predictions. As shown in FIG. 12, for this example in month 0 the sales for the previous game version was 25,000 units, the sales for similar game titles were 30,000 units and the publisher prediction was 30,000 units. Given these values the forecast for month 0 is equal to 27,500 units (i.e. (25,000*0.50)+(30,000*0.30)+(30,000*0.20)=27,500 units). It is contemplated that other methods for generating weight values may also be used.

At this point, the “base sales curve” is modified to generate a “final sales curve.” It should be appreciated that although for this example only one modifier was used to show the process, any number of modifiers (include zero) may be applied in the same manner. For this example a “seasonal sales modifier,” as illustrated by the graph shown in FIG. 13, was used, where the “seasonal sales modifier” indicates how sales are affected by the time of year. However, the sales forecast may be fixed to a specific period of time. This is possible because the date the game will be released is typically known and may correspond to the beginning of month 0 of the sales forecast as shown in FIG. 14.

The modifier curve may be applied to the sales forecast, for example one way may include aligning the time periods on both curves, and replicating the modifier curve to span the entire period of the forecast. Referring to FIG. 15, the “final sales curve” (i.e. the thicker line) is generated by multiplying the “base sales curve” by the “modifier curve.” At this point, the life-cycle curve is created by combining the game-play curve and the final sales curve. This process may be better understood and illustrated by describing the process of combining the curves, rather than by showing it in graphical form, where the process is an iteration over every day in the “final sales curve” (e.g. the time period in this example). For each day, the “game-play curve” may be multiplied by the number of units to be sold that day. The monthly values of the sales curve may then be extrapolated to daily values by spreading them evenly throughout the month (i.e. 30,000 units in the month of April would amount to 1,000 units sold each day on average). It is contemplated that this may be accomplished via any mathematical method to more accurately match the slope of the sales curve. In this example, although the monthly values are distributed evenly throughout the month to simplify the process, it is contemplated that these may be distributed any way suitable to the desired end purpose.

For example, if sales for the month of January for a given year are predicted to be 33,000 units, it would be expected that about 1,065 units per day would be sold that month (i.e. 33,000/31). The first day of the set over which we are iterating is January 1^(st), so the “game-play curve” is multiplied by that number of units to be sold on January 1^(st) (i.e. 1,065) which will produce a curve as shown in FIG. 16. This resultant curve may be viewed as the start of our life-cycle curve. The second day of the iteration is January 2^(nd), with another 1,065 units expected to be sold. Again, the “game-play curve” is multiplied by that value and the resultant value is added to our life-cycle curve with day 0 indicating January 2^(nd), as shown in FIG. 17. This iteration is continued until the “final life-cycle curve” is created, as shown in FIG. 18.

Next, modifiers to the “final life-cycle curve” may be applied in the same manner as was done with the “final sales curve” to generate the forecast data. Referring to FIG. 19, for this example a “day of week modifier” is applied to the “final life-cycle curve.” It should be appreciated that the overall resultant final curve (forecast data) is difficult to illustrate since the modifier causes the curve to have peaks and valleys on a weekly cycle. However, for illustrative purposes a section of the resultant final curve is shown in FIG. 20 for the first month of the life-cycle, in this case January. At this point in the forecasting process the number of hours expected to be played may be converted to a value which indicates the number of impressions expected. For this example, it is assumed that the game title being forecast has been tested and generates an average of seven (7) impressions for each hour that it is played. To perform the conversion for this example, the “final life-cycle curve” is multiplied by this value (i.e. “7”). The resulting “impression forecast curve” is shown in FIG. 21 and typically indicates the number of impressions expected per day.

It should be appreciated that once the game title is released, the actual impression and/or user data that is collected may be compared to the predicted impressions and/or user values (i.e. the “final sales curve,” “impression forecast curve,” etc.). This may be done by taking representative samples of users playing the game, generating an “actual game-play curve” and/or generating an “actual sales curve.” These curves may then undergo the same procedure as described hereinbefore, with the possible exception of the modifiers since the modifiers may be implicit in the actual data, to create an “actual impression curve.” However, it is contemplated that modifiers may or may not be used as desired. The resulting curve may then be combined with the “impression forecast curve” using a weighted average (in the same or similar manner as for creating the “base sales curve”). As discussed hereinbefore, the weighting values for this average may start out highly in favor of the forecast curve and decrease as time goes on (e.g. 95% to 5%) since very little actual data is immediately available. Since this process occurs on a regular basis (perhaps at least once a day), the amount of actual data will typically increase, as will it's reliability. Over time, as more actual data is obtained, the weighting values for the forecast curve may be reduced while the weighting value for the actual curve may be increased until they each reach approximately 50% each. The result from this averaging may then become the new forecast curve.

It should be appreciated that the method of the present invention may be embodied, in whole or in part, via software, firmware and/or hardware, and that that any type of application software may be used to practice the present invention. Moreover, the invention may be implemented via any type or configuration of software suitable to the desired end purpose, such as a generic SDK and/or an application specific SDK. Furthermore, the software application may or may not be embedded, in whole or in part. Additionally, it should also be appreciated that the method of the present invention may or may not be embodied, in whole or in part, via instruction using training manuals (i.e. text based materials), seminars, classes, and/or any other media suitable to the desired end purpose. Moreover, it should be appreciated that although the method of the present invention may be implemented, in whole or in part, via software, hardware, firmware and/or any combination thereof, it is also contemplated that the method of the present invention may also be implemented, in whole or in part, without the use of software, hardware, firmware and/or any combination thereof. For example, without the full or partial use of any software, hardware and/or firmware and/or with any combination thereof, but rather via instruction using PC based software and/or classroom instruction with text materials (i.e. books, pamphlets, handouts, tapes, optical media, etc.).

Moreover, it should be appreciated that each of the elements of the present invention may be implemented in part, or in whole, in any order suitable to the desired end purpose. In accordance with an exemplary embodiment, the processing required to practice the method of the present invention, either in whole or in part, may be implemented, wholly or partially, by a controller operating in response to a machine-readable computer program. In order to perform the prescribed functions and desired processing, as well as the computations therefore (e.g. execution control algorithm(s), the control processes prescribed herein, and the like), the controller may include, but not be limited to, a processor(s), computer(s), memory, storage, register(s), timing, interrupt(s), communication interface(s), and input/output signal interface(s), as well as combination comprising at least one of the foregoing. It should also be appreciated that the embodiments disclosed herein are for illustrative purposes only and include only some of the possible embodiments contemplated by the present invention.

Furthermore, the invention may be wholly or partially embodied in the form of a computer or controller implemented processes. It should be appreciated that any type of computer system (as is well known in the art) and/or gaming system may be used and that the invention may be implemented via any type of network setup, including but not limited to a LAN and/or a WAN (wired or wireless). The invention may also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, and/or any other computer-readable medium, wherein when the computer program code is loaded into and executed by a computer or controller, the computer or controller becomes an apparatus for practicing the invention. The invention can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein when the computer program code is loaded into and executed by a computer or a controller, the computer or controller becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor the computer program code segments may configure the microprocessor to create specific logic circuits.

While the invention has been described with reference to an exemplary embodiment, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, unless specifically stated any use of the terms first, second, etc. do not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another. 

1. A method for forecasting a performance characteristic of a game title, the method comprising: selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user; generating base sales data for the game title responsive to initial sales data; and generating forecast data for the game title responsive to the base sales data and the base game-play data.
 2. The method of claim 1, wherein said initial sales data includes at least one of said sales forecast data, sales data from previous versions of the game title and sales data from similar game titles.
 3. The method of claim 2, wherein generating base sales data includes combining said at least one of said sales forecast data, sales data from previous versions of the game title and sales data from similar game titles.
 4. The method of claim 1, wherein generating forecast data includes generating final sales data responsive at least in part to the base sales data.
 5. The method of claim 4, wherein generating final sales data includes combining the base sales data with sales modifier data, wherein said sales modifier data includes at least one of seasonal sales modifier data, marketing modifier data, piracy modifier data, pricing modifier data and buzz modifier data.
 6. The method of claim 4, further comprising combining the final sales data and the base game-play data to generate life-cycle data.
 7. The method of claim 6, further comprising combining the life-cycle data with a life-cycle modifier data to generate the forecast data.
 8. The method of claim 7, wherein life-cycle modifier data includes data that relates the number of hours the game is played to the time frame that the game is played.
 9. The method of claim 5, wherein sales modifier data includes data that relates the number of units sold to the amount of time the game is played.
 10. A system for implementing a method for forecasting the performance of a game title, the system comprising: a means for selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user; a means for generating base sales data for the game title responsive to initial sales data; and a means for generating forecast data for the game title responsive to the base sales data and the base game-play data.
 11. The system of claim 10, wherein said initial sales data includes at least one of said sales forecast data, sales data from previous versions of the game title and sales data from similar game titles.
 12. The system of claim 11, wherein generating base sales data includes combining said at least one of said sales forecast data, sales data from previous versions of the game title and sales data from similar game titles.
 13. The system of claim 10, wherein generating forecast data includes generating final sales data responsive at least in part to the base sales data.
 14. The system of claim 13, wherein generating final sales data includes combining the base sales data with sales modifier data, wherein said sales modifier data includes at least one of seasonal sales modifier data, marketing modifier data, piracy modifier data, pricing modifier data and buzz modifier data.
 15. The system of claim 13, further comprising combining the final sales data and the base game-play data to generate life-cycle data.
 16. The system of claim 15, further comprising combining the life-cycle data with a life-cycle modifier data to generate the forecast data.
 17. The system of claim 16, wherein life-cycle modifier data includes data that relates the number of hours the game is played to the time frame that the game is played.
 18. The method of claim 14, wherein sales modifier data includes data that relates the number of units sold to the amount of time the game is played.
 19. A computer readable storage medium having computer executable instructions for implementing a method for forecasting the performance of a game title, the method comprising: selecting base game-play data for the game title, wherein the base-game play data is at least partially responsive to the game-play pattern of a user; generating base sales data for the game title responsive to initial sales data; and generating forecast data for the game title responsive to the base sales data and the base game-play data.
 20. The computer readable storage medium of claim 19, wherein generating forecast data includes, combining the base sales data with sales modifier data to generate final sales data; processing the final sales data with the base game-play data to generate life-cycle data; and combining the life-cycle data with life-cycle modifier data to generate the forecast data. 